Intention estimation method and system with confidence indication

ABSTRACT

An intention estimation system and method with confidence indication for providing operation assistance based on an operator&#39;s intention. A current operation performed by the operator is detected, and data related to an estimated intention of the operator is generated based on the detected operation. A confidence index of the estimated intention of the operator is then determined. The confidence index indicates, for example, the reliability or strength of the estimated intention. An operation assistance may be provided based on the estimated intention and the confidence index.

RELATED APPLICATIONS

The present application claims the benefit of priority from Japanesepatent application No. 2003-417746, filed Dec. 16, 2003, the disclosureof which is incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to estimation of an operator's intentionand providing operation assistance, and more particularly, to a driver'sintention estimation method and system with a confidence indication.

BACKGROUND OF THE DISCLOSURE

A number of methods and systems have been proposed for providingassistance in operating a device, system or machine, such as a vehicle.For example, several driving assistance systems were disclosed in U.S.Published Patent Application Nos. 20030060936 A1, published Mar. 27,2003, and 20040172185 A1, published Sep. 2, 2004. In order to enhanceperformance, some driving assistance systems may require estimation of adriver's intention in driving a vehicle. A system for estimating adriver's intention may collect estimates of the driver's intention usingmovement of the driver's eyeballs. For example, directions to which thedriver's eyeballs turn are projected onto a plane divided into a numberof regions, for calculating a distribution of projected directions overthe divided regions to estimate the driver's intention. However, suchtype of systems lacks accuracy because the driver's eyeballs move allthe time and do not always relate to a “driving” intention of thedriver.

Therefore, there is a need for reliable intention estimation systemsthat can estimate an operator's intention with satisfactory accuracy.There is also a need for determining how reliable or how strong anestimated intention is, such that operation assistance can be providedaccordingly.

SUMMARY OF THE DISCLOSURE

This disclosure presents system, control process and method that provideeffective estimation of an operator's intention in operating a device,system or machine, and providing a confidence index to indicate thereliability or degree of confidence of the estimated intention of theoperator. Operation assistance may be provided based on the confidenceindex. The advantages, operations and detailed structures of thedisclosed methods and systems will be appreciated and understood fromthe descriptions provided herein.

An exemplary system according to this disclosure detects an operationperformed by an operator of a machine, and generates data related to anestimated intention to approximate the intention of the operator basedon the detected operation. The operation may correspond to multiplepossible intentions retained by the operator. A confidence calculator isprovided to determine a confidence index of the estimated intention. Forinstance, the confidence index may represent a period of time that theoperator has retained the estimated intention. The longer the operatorretains the intention, the more determined the operator is to perform anaction or operation according to the estimated intention.

The estimated intention may be obtained using various approaches. In oneembodiment, an intention estimation device is provided to calculate anestimated intention of the operator. In one aspect, the intentionestimation device includes a first device configured to provide datarelated to a plurality of imaginary operators, each of the plurality ofimaginary operators associated with at least one intention, wherein eachof the at least one intention is associated with an operation of therespective imaginary operator; and a second device configured tocalculate a likelihood value for each of the plurality of imaginaryoperators based on the detected operation of the operator and therespective associated operation of each of the plurality of imaginaryoperators. An additional third device is provided to generate theestimated intention of the operator based on the respective likelihoodvalue of each of the plurality of imaginary operators. The confidencecalculator may calculate the confidence index of the estimated intentionbased on the respective likelihood value of each of the plurality ofimaginary operators.

In another aspect, the intention estimation device generates theestimated intention of the operator based on the detected operation ofthe driver and reference data related to predetermined operationpatterns. The intention estimation device may generate the estimatedintention by applying one of a support vector machine and a relevancevector machine to data related to the detected operation and thereference data related to the predetermined operation patterns.

According to one embodiment of this disclosure, a control device isprovided to regulate the operation of an operation device of a themachine based on the confidence index. A vehicle may implement a systemaccording to this disclosure to regulate the operation of an operationdevice of the vehicle, such as an accelerator pedal or a braking system.The control device modifies a reaction force of the accelerator pedal ofthe vehicle or a deceleration force of the braking system. According toanother embodiment, the vehicle includes a risk calculation deviceconfigured to calculate a risk potential associated with the vehicle.The operation device of the vehicle is regulated based on the calculatedrisk potential associated with the vehicle and the confidence index. Inone aspect, the control device modifies the risk potential based on theconfidence index, and regulates the operation of the operation devicebased on the modified risk potential. In another aspect, the controldevice calculates a regulation amount to regulate the operation of theoperation device based on the risk potential, and modifies thecalculated regulation amount based on the confidence index.

The system and method described herein may be implemented using one ormore data processing devices, such as controllers or microcomputers,executing software programs and/or microcode.

Additional advantages of the present disclosure will become readilyapparent to those skilled in this art from the following detaileddescription, wherein only the illustrative embodiments are shown anddescribed, simply by way of illustration of the best mode contemplated.As will be realized, the disclosure is capable of other and differentembodiments, and its several details are capable of modifications invarious obvious respects, all without departing from the disclosure.Accordingly, the drawings and description are to be regarded asillustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawing and in whichlike reference numerals refer to similar elements and in which:

FIG. 1( a) is a block diagram illustrating an exemplary implementationof an intention estimation system according to the present disclosure.

FIG. 1( b) is a block diagram illustrating another exemplaryimplementation of an operation assistance system according to thepresent disclosure.

FIG. 2 is a flow chart illustrating operation of the intentionestimation system illustrated in FIG. 1( a).

FIG. 3 illustrates calculation of an operation amount for an imaginarydriver.

FIG. 4 is an exemplary illustration of generating a series oflane-keeping intentions retained by a parent imaginary driver andderivative lane-change intentions retained by additional imaginarydrivers.

FIG. 5( a) shows a rule for applying to the generation of data relatedto imaginary drivers as illustrated in FIG. 4.

FIG. 5( b) shows another rule for applying to the generation of datarelated to imaginary drivers as illustrated in FIG. 4.

FIG. 6 is an exemplary illustration of three series of intentions andrespective likelihood values associated with additional imaginarydrivers, each series has a lane-change intention to the right (LCR) asthe most recent intention.

FIG. 7 is a flow chart illustrating operation of the driver assistingsystem illustrated in FIG. 1( b).

FIG. 8 illustrates characteristics of a reaction force increment ΔFrelative to different values of risk potential (RP).

FIG. 9 illustrates characteristics of a time constant Tsf relative todifferent values of a confidence index Sc.

FIG. 10 is a block diagram illustrating another exemplary implementationof a driver assisting system according to the present disclosure.

FIG. 11 is a perspective view a vehicle in the form of an automobileincorporating the driver assisting system.

FIG. 12 is an illustration of an operation, in the form of anaccelerator pedal, of the vehicle.

FIG. 13 is a flow chart illustrating operation of the driver assistingsystem illustrated in FIG. 10.

FIG. 14 illustrates characteristics of a reaction force increment ΔFrelative to different values of risk potential (RP).

FIG. 15 illustrates characteristics of a time constant Tsf (or Tsg)relative to different values of a confidence index Sc.

FIG. 16 illustrates characteristics of a deceleration instruction valueXg relative to different values of risk potential (RP).

FIG. 17( a) illustrates a traffic scene in which a vehicle changes lanesto pass the preceding vehicle.

FIG. 17( b) illustrates a corrected accelerator pedal reaction forceinstruction value FAc in response to the estimated driver's lane-changeintention.

FIG. 17( c) illustrates varying a corrected deceleration instructionvalue Xgc in response to the estimated driver's lane-change intention.

DETAILED DESCRIPTION OF THE DISCLOSURE

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,to one skilled in the art that the present method and system may bepracticed without these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the present disclosure. For illustrationpurpose, the following examples describe the operation of an exemplarytester used for evaluating a circuit of an automotive vehicle. It isunderstood that the use of tester is not limited to vehicle circuits.The tester also can be used in other types of electrical circuits.

First Exemplary Implementation of the Disclosure

Referring to FIG. 1( a), an exemplary intention estimation system 1 fora vehicle includes a vehicle's environment detector 10, a vehicle'sstatus detector 20, a real driver's operation detector 30, an imaginarydriver's intention generating section 40, an imaginary driver'soperation calculator 50, a likelihood P(j)ids calculator 60, a driver'sintention estimator 70, and a confidence index estimator 80. Thevehicle's environment detector 10 detects a state of environmentsurrounding the vehicle. The vehicle's status detector 20 detects astatus of the vehicle. The real driver's operation detector 30 detectsan operation amount of a real driver in driving the vehicle.

The driver's intention estimating system 1 has access to reference data,such as data related to a plurality of imaginary drivers. In operatingan operation device of the vehicle, such as an acceleration pedal orsteering wheel, each imaginary driver is designed to perform anoperation of the vehicle according to an associated intention. Examplesof the intention include a lane-keeping intention (LK), a lane-changeintention to the right (LCR), and a lane-change intention to the left(LCL). As will be described later in connection with FIGS. 4, 5(a), and5(b), the imaginary driver's intention generating section 40continuously generates a lane-keeping intention (LK) at every point intime to form a series of intentions for a parent imaginary driver.Furthermore, the imaginary driver's intention generating section 40generates data related to at least one additional imaginary driver basedon the intention of the parent imaginary driver. In one embodiment, theimaginary driver's intention generating section 40 generates datarelated to two additional imaginary drivers, each has one of twoderivative lane-change intentions (LCR) and (LCL) based on alane-keeping intention (LK) of the parent imaginary driver at animmediately preceding point in time. In another embodiment, theimaginary driver's intention generating section 80 applies special rulesin generating series of intentions for the additional imaginary drivers.

The imaginary driver's intention generating section 40 allows a parentimaginary driver to retain a lane-keeping intention (LK) at every pointin time. Further, at every point in time with the parent imaginarydriver having a lane-keeping intention (LK), the imaginary driver'sintention generating section 40 generates data related to two additionalimaginary drivers having lane-change intentions to the right (LCR) andto the left (LCL), respectively, for the next point in time. In oneembodiment, an additional imaginary driver generated at a specific pointof time assumes at least some of the intentions for all points of timepreceding the specific point in time, from the parent imaginary driver.

Moreover, the imaginary driver's intention generating section 40determines whether or not an imaginary driver retaining one of thederivative lane-change intentions to exist at the next point in timeshould be allowed to continue to exist, by applying one or more rules.For instance, an exemplary rule allows the parent imaginary user toretain a lane-keeping intention (LK) at every point in time, andgenerates data related to two additional imaginary drivers havinglane-change intentions (LCR) and (LCL), respectively, at the next pointin time. According to another exemplary rule, an imaginary driver isallowed to retain a lane-change intention to the right (LCR) at the nextpoint in time if it is determined that the real driver continues toretain a lane-changing intention at the present point in time. On theother hand, if it is determined that at a specific point in time, thereal driver no longer wants to change lanes or has just changed lanes,an imaginary driver is not allowed to retain a lane-change intention tothe right (LCR) at the next point in time . This is equally applicableto a lane-change intention to the left (LCL). Accordingly, an imaginarydriver having a lane-change intention to the left (LCL) at a specificpoint in time is allowed to retain a lane-change intention to the left(LCL) at the next point in time upon determination that a lane changecontinues, but the imaginary driver is not allowed to continue to retaina lane-change intention to the left (LCL) at a specific point in timeupon failure to determine that the lane change continues. Therefore, animaginary driver that has one of the derivative lane-change intentions(LCR) and (LCL), is allowed to retain the derivative lane-changeintention at the next point in time upon determination that a lanechange continues.

At each point in time, each of the imaginary drivers has an associatedoperation corresponding to an intention retained by that imaginarydriver. The process for determining an operation associated with eachintention is described below.

The vehicle's environment detector 10 provides information on a state ofenvironment around the vehicle to the imaginary driver's operationcalculator 50. Examples of such information include a lateral distance yof the vehicle from a centerline within a lane, and a yaw angle ψ of thevehicle with respect a line parallel to the centerline. The vehicle'sstatus detector 20 provides information on a status of the vehicle tothe imaginary driver's operation calculator 50. Examples of suchinformation include a vehicle speed of the vehicle and a steering angleof the vehicle.

The imaginary driver's operation calculator 50 calculates operationamounts Oid of the imaginary drivers in a manner that will be describedin detail in connection with FIG. 3. In order to reduce the computationload, certain rules are applied to determine whether an existingadditional imaginary driver retaining one or the derivative lane-changeintentions should be allowed to exist at the next point in time. Inother words, thief a predetermined condition established by the rules isnot met by an additional imaginary driver at a specific point in time,that additional imaginary driver is terminated or eliminated. Since itis not necessary to calculate operation amounts Oid of the eliminatedimaginary drivers, the computation load is reduced.

The imaginary driver's operation calculator 50 provides the calculatedoperation amounts Oid of the imaginary drivers to the likelihood valueP(j)ids calculator 60. For comparison with each of the calculatedoperation amounts Oid of the imaginary drivers, the real driver'soperation detector 30 provides a detected operation amount Ord to thelikelihood value P(j)ids calculator 60. An example of the operationamount to be detected is a steering angle of the vehicle.

The likelihood value P(j)ids calculator 60 calculates a likelihood valuePid(j)(t) of an imaginary driver, based on the associated operationamounts Oid and the detected operation amount Ord. The calculatedlikelihood values Pid(j)(t) are stored in data storage devices, such asmemory or hard disk. For each imaginary driver, the data storage devicestores the most recently calculated likelihood value Pid(j)(t) aftershifting the previously calculated likelihood value. The storedlikelihood values may be represented in the form of Pid(j)(t),Pid(j)(t−1), . . . , Pid(j)(t−m+1), which correspond to likelihoodvalues calculated at different points in time ranging from time (t) backto time (t−m+1). The m, in number, points in time are arranged atregular intervals and define a predetermined period of time.

The likelihood value P(j)ids calculator 60 calculates a collectivelikelihood value P(j)ids for each imaginary driver j based on likelihoodvalues Pid(j)(t), Pid(j)(t−1), . . . , Pid(j)(t−m+1) and provides thecalculated series-likelihood values P(j)ids for processing at thedriver's intention estimator 70.

In one embodiment, the driver's intention estimator 70 selects one ofthe imaginary drivers to approximate behaviors of the real driver basedon the calculated collective likelihood values P(j)ids. An intention ofthe selected imaginary driver is set as an estimated driver's intentionλrd.

The confidence index estimator 80 estimates a confidence index of theestimated intention of the real driver.

With continuing reference to FIG. 1( a), the vehicle's environmentdetector 10 includes a front camera that covers a field of front viewand a yaw angle sensor. The front camera acquires image on roadconditions, for example, within the field of front view. The vehicle'senvironment detector 10 detects a lateral distance y of the vehicle froma centerline within a lane, and a yaw angle ψ of the vehicle withrespect a line parallel to the centerline. The vehicle's environmentdetector 10 is equipped with an image processor that processes theacquired image. The vehicle's status detector 20 includes a vehiclespeed sensor for detecting a speed of the vehicle. The real driver'soperation detector 30 includes a sensor to detect an operation performedby the driver. Detector 30 may be a steering angle sensor that detects asteering angle of the vehicle. Other types of sensor also can be used,such as an acceleration sensor or brake sensor.

In the exemplary implementation, an exemplary system includes imaginarydriver's intention generating section 40, imaginary driver's operationcalculator 50, likelihood value P(j)ids calculator 60, driver'sintention estimator 70, and confidence index estimator 80. Some or allof these elements are implemented using one or more microcomputers ormicrocontrollers, such as a central processor unit (CPU), executingmicrocode, software programs, and/or instructions. The microcode and/orsoftware reside in volatile and/or non-volatile data storage devicesand/or machine-readable data storage medium such as read only memory(ROM) devices, random access memory (RAM) devices, SRAM, PROM, EPROM,CD-ROM, disks, carrier waves, etc.

As described before, the imaginary driver's intention generating section40 continuously generates data related to imaginary drivers. Each of theimaginary drivers retains a series of intentions over a period of timethat the number of the imaginary drivers and the types of intentionsretained by the imaginary drivers are dynamic and may change over time.

As described before, the imaginary driver's operation calculator 50calculates operation amounts Oid of the imaginary drivers associatedwith different intentions that are determined by the imaginary driver'sintention generating section 40.

The real driver's intention estimator 70 determines an estimatedintention of the real driver after comparing the likelihood values ofthe imaginary drivers, which are calculated based on the operationamounts for each imaginary driver over a period of time and theoperation amount of the real driver detected over the same period oftime.

The confidence index estimator 80 estimates a confidence index for theestimated intention of the real driver.

Referring to FIGS. 1( b), 11 and 12, a driver assisting system 100 isdescribed.

For better understanding of the driver assisting system, referenceshould be made to U.S. Published Patent Application No. 2003/0060936 A1,published Mar. 27, 2003, which is incorporated herein by reference inits entirety.

The driver assisting system 100 includes a laser radar 110. The laserradar 110 is mounted to an automobile at a front bumper or a frontgrille thereof. Lacer radar 110 scans horizontally and laterally about 6degrees to each side of an axis parallel to the vehicle longitudinalcenterline, propagates infrared pulses forwardly and receives thereflected radiation by an obstacle, such as, a rear bumper of apreceding vehicle. The laser radar 110 can provide a distance d to apreceding vehicle in front and a relative speed Vr to the precedingvehicle. The laser radar 110 provides, as outputs, the detected distanced and relative speed Vr to a controller 150. The driver assisting system100 also includes a front camera 120. The front camera 120 is CCD typeor CMOS type, and mounted to the vehicle in the vicinity of the rearview mirror to acquire image data of a region in front of the vehicle asshown in FIG. 11. The front camera 120 provides, as output signals, theacquired image data to an image processor 130. The image processor 130provides the processed image data to the controller 150. The regioncovered by the front camera 120 extends from the camera axis to eachside by 30 degrees.

The driver assisting system 100 also includes a vehicle speed sensor140. The vehicle speed sensor 140 determines a vehicle speed of the hostvehicle by processing outputs from wheel speed sensors. The vehiclespeed sensor 140 may include an engine controller or a transmissioncontroller, which can provide a signal indicative of the vehicle speed.The vehicle speed sensor 140 provides, as an output, the vehicle speedof the host vehicle to the controller 150.

The driver assisting system 100 also includes a driver's intentionestimating system 1 as illustrated in FIG. 1( a) to provide an estimatedreal driver's intention λrd and a confidence index Sc to the controller150.

The controller 150, which performs data processing within the driverassisting system 100, may contain a microprocessor including a centralprocessing unit (CPU), a read only memory (ROM), and a random accessmemory (RAM). The controller 150 includes, for example, softwareimplementation of a risk potential (RP) calculator 151, an acceleratorpedal reaction force instruction value FA calculator 152, and aninstruction value FA correcting section 153.

The RP calculator 151 calculates a risk potential (RP) associated withthe vehicle based on the vehicle's environment using a vehicle speed V1of the host vehicle, a distance D to the preceding vehicle, and arelative speed Vr to the preceding vehicle, which are provided by thelaser radar 110, vehicle speed sensor 140 and image processor 130. TheRP calculator 151 provides, as an output, the risk potential RP to theaccelerator pedal reaction force instruction value FA calculator 152.

The accelerator pedal reaction force instruction value FA calculator 152calculates an accelerator pedal reaction force instruction value FAbased on the risk potential RP. The accelerator pedal reaction forceinstruction value FA calculator 152 provides, as an output, theaccelerator pedal reaction force instruction value FA to the instructionvalue FA correcting section 153.

The instruction value FA correcting section 153 corrects the acceleratorpedal reaction force instruction value FA based on the estimateddriver's intention λrd and the confidence index Sc, and generates acorrected accelerator pedal reaction force instruction value FAc. Theinstruction value FA correcting section 153 provides, as an output, thecorrected accelerator pedal reaction force instruction value FAc to anaccelerator pedal reaction force control unit 170.

In response to the corrected accelerator pedal reaction forceinstruction value FAc, the accelerator pedal reaction force control unit170 regulates a servo motor 180 of an accelerator pedal 160 (see FIG.12). As shown in FIG. 12, the accelerator pedal 160 has a link mechanismincluding a servo motor 180 and an accelerator pedal stroke sensor 181.The servo motor 180 may provide any desired torque and any desiredangular position in response to an instruction from the acceleratorpedal reaction force control unit 170. The accelerator pedal strokesensor 181 detects an accelerator pedal stroke or position S of theaccelerator pedal 160 by measuring an angle of the servo motor 180. Theangle of the servo motor 180 corresponds to the accelerator pedal strokeS because the servo motor 180 and the accelerator pedal 160 areinterconnected by the link mechanism.

For better understanding of the accelerator pedal of the above kind,reference are made to U.S. Published Patent Application No. 2003/0236608A1 (published Dec. 25, 2003) and 2003/0233902 A1 (published Dec. 25,2003), both of which are incorporated herein by reference in theirentireties.

When the accelerator pedal reaction force control unit 170 is notactive, the reaction force increases linearly as the accelerator pedalstroke S increases. This reaction force varying characteristic isaccomplished by a spring force provided by a torque spring arranged atthe center of rotational movement of the accelerator pedal 160.

Referring to FIGS. 2 and 3, the operation of the driver's intentionestimating system 1 is explained. The flow chart in FIG. 2 illustrates adriver's intention estimation processing program. Execution of thisprogram is repeated at a regular interval of ΔT, for example, ΔT=50milliseconds.

At step S101, the microcomputer reads in data related to a lateralposition y of the vehicle within a lane (or track) and a yaw angle ψ ofthe vehicle. As shown in FIG. 3, the lateral position y is a distance ofa center O of the vehicle from the centerline of the lane, and the yawangle ψ is an angle through which the vehicle is turned relative to aspecific reference, such as a line parallel to the centerline of thelane.

At step S102, the microcomputer calculates an operation Oid of each of aplurality of imaginary drivers. In this example, the plurality ofimaginary drivers are variable in number and includes an imaginarydriver A designed to behave as directed by the latest intention of amother series of a lane-keeping intention (LK). The remaining of theplurality of imaginary drivers consists of at least one imaginary driverB designed to behave as directed by a lane-change intention to the right(LCR), and at least one imaginary driver C designed to behave asdirected by a lane-change intention to the left (LCL). The microcomputercalculates an operation amount Oid, by which each of these threeimaginary drivers A, B and C would operate an operation device indriving the vehicle as directed by the intention. In the exemplaryimplementation, the operation device is a steering system of thevehicle. In this case, the operation amount Oid is a steering angle θid.More particularly, the microcomputer calculates a steering angle θid,which each of the three imaginary drivers A, B and C would perform tomanipulate a steering wheel in driving the vehicle as directed by theintention. The following descriptions describe how a steering angle θidassociated with an imaginary driver is calculated.

(1) Imaginary Driver A Having A Lane-Keeping Intention (LK):

Steering angle θid.lk represents an angle that imaginary driver A havinga lane-keeping intention (LK) would manipulate a steering wheel indriving the vehicle as directed by the lane-keeping intention (LK). Themicrocomputer sets at least one reference point LK(i) in front on alongitudinal centerline of the vehicle at a distance px(i) from thecenter O of the vehicle, and calculates a lateral position p.lk(px(i))of the reference point LK(i) from a centerline of a lane. At least onereference point LK(i) includes any desired number of reference pointsLK(i). In this example, as shown in FIG. 3, two reference points LK(1)and LK(2) are set on the longitudinal centerline of the vehicle atdifferent distances px(1) and px(2) from the center O of the vehicle,wherein the distance px(1)=10 m and the distance px(2)=30 m. Thedistance px(i) may have varying values with different vehicle speeds.

A lateral distance lat.pos(px(i)) of the reference point LK(i) from thecenterline of the lane is dependent on, and is determined by, the yawangle ψ and the distance px(i), which may be determined, for example, byprocessing the acquired image from the front camera. Thus, the lateralposition p.lk(px(i) of the reference point LK(i) may be expressed as:p.lk(px(i)=lat.pos(px(i)) i={1, . . . , n}  (Eq. 1)The number n is equal to 2 (n=2) in the example shown in FIG. 3.

Using the lateral position p.lk(px(i)), the steering angle θid.lk may beexpressed as:θid.lk=Σ{a(i)·p.lk(px(i))}  (Eq. 2)

where: a(i) is an appropriately determined coefficient weighting thelateral position p.lk(px(i)), and is determined based on characteristicsof vehicles, such as the gear ratio of a vehicle implementing the systemdisclosed herein.

(2) Imaginary Driver B Having a Lane-Change Intention to the Right(LCR):

Steering angle θid.lcr represents an angle that imaginary driver Bhaving a lane-change intention to the right (LCR) would manipulate asteering wheel in driving the vehicle as directed by the lane-changeintention to the right (LCR). The microcomputer sets at least onereference point LCR(i), which may include any desired number ofreference points LCR(i). In this example, as shown in FIG. 3, tworeference points LCR(1) and LCR(2) are set.

A lateral position p.lcr(px(i)) of the reference point LCR(i) may begiven as a sum of lat.pos(px(i)) and a predetermined offsetlc.offset.lcr. Lateral position p.lcr(px(i)) can be expressed as:p.lcr(px(i)=lat.pos(px(i))+lc.offset.lcr i={1, . . . , n}  (Eq. 3)The number n is equal to 2 (n=2) in the example shown in FIG. 3. Thepredetermined offset lc.offset.lcr is an appropriately determined valuefor giving the lateral position p.lcr(px(i)) of the reference pointLCR(i). In this example, the offset lc.offset.lcr is equal to −1.75(lc.offset.lcr=−1.75).

Using the lateral position p.lcr(px(i)), the steering angle θid.lcr maybe expressed as:θid.lcr=Σ{a(i)·p.lcr(px(i))}  (Eq. 4)

where: a(i) is an appropriately determined coefficient weighting thelateral position p.lcr(px(i)), and is determined based oncharacteristics of vehicles, such as the gear ratio of a vehicleimplementing the system disclosed herein.

(3) Imaginary Driver C Having Lane-Change Intention to the Left (LCL):

Steering angle θid.lcl represents an angle by which an imaginary driverC having a lane-change intention to the left (LCR) would manipulate asteering wheel in driving the vehicle as directed by the lane-changeintention to the left (LCR). The microcomputer sets at least onereference point LCL(i), which may include any desired number ofreference points LCL(i). In this example, as shown in FIG. 3, tworeference points LCL(1) and LCL(2) are set.

A lateral position p.lcl(px(i)) of the reference point LCL(i) may begiven by a sum of lat.pos(px(i)) and a predetermined offsetlc.offset.lcl, and thus expressed as:p.lcl(px(i))=lat.pos(px(i))+lc.offset.lcl i={1, . . . , n}  (Eq. 5)The number n is equal to 2 (n=2) in the example shown in FIG. 3. Thepredetermined offset lc.offset.lcl is an appropriately determined valuefor giving the lateral position p.lcl(px(i)) of the reference pointLCL(i). In this example, the offset lc.offset.lcl is equal to 1.75(lc.offset.lcr=1.75).

Using the lateral position p.lcl(px(i)), the steering angle θid.lcl maybe expressed as:θid.lcl=Σ{a(i)·p.lcl(px(i))}  (Eq. 6)

where: a(i) is an appropriately determined coefficient weighting thelateral position p.lcl(px(i)), and is determined based oncharacteristics of vehicles, such as the gear ratio of a vehicleimplementing the system disclosed herein.

After calculating the operation amount Oid of each of the imaginarydrivers A, B and C at step S102, the logic goes to step S103. At stepS103, the microcomputer receives, as an input, an operation amount Ordof a real driver by, in this exemplary implementation, reading in asteering angle θrd detected by the real driver's operation detector 30.

At the next step S104, the microcomputer forms a series of intentionsfor each of the plurality of imaginary drivers. The types of intentionsand the number of the imaginary drivers may change over time. Themicrocomputer has memory portions for storing the intentions of theimaginary drivers. Each of the memory portions is designed to store m,in number, intentions over a period of time ranging from time (t) backto time (t−m+1). Except for a special memory portion, the microcomputerresets any one of the remaining memory portions upon determination thatthe memory portion has contained m, in number, intentions of the samekind.

FIG. 4 illustrates data related to a plurality of imaginary driversgenerated by the microcomputer. Each imaginary driver retains a seriesof intentions over, m, in number, points in time from time (t) back totime (t−m+1). Referring to FIG. 4, the microcomputer continuouslygenerates a lane-keeping intention (LK) at every point in time. Thelane-keeping intentions form a series of intentions assigned to a parentimaginary driver.

Furthermore, the microcomputer generates data related to at least oneadditional imaginary driver based on the intention of the parentimaginary driver. In the example shown in FIG. 5( a), the microcomputergenerates data related to two additional imaginary drivers, each has oneof two derivative lane-change intentions (LCR) and (LCL) based on alane-keeping intention (LK) of the parent imaginary driver at animmediately preceding point in time. In addition, the two additionalimaginary drivers generated at a specific point of time assumes at leastsome of the intentions for all points in time preceding the specificpoint in time, from the parent imaginary driver.

Referring also to FIG. 5( a), the microcomputer applies certain rules ingenerating series of intentions for existing additional imaginarydrivers. For instance, the microcomputer determines whether an imaginarydriver retaining one of the derivative lane-change intentions maycontinue to exist at the next point in time, by applying one or morerules. An exemplary rule allows the parent imaginary driver to retain alane-keeping intention (LK) at every point in time, and generates datarelated to two additional imaginary drivers having lane-changeintentions (LCR) and (LCL), respectively, at the next point in time.According to another exemplary rule, an imaginary driver is allowed toretain a lane-change intention to the right (LCR) at the next point intime if it is determined that the real driver continues to retain alane-changing intention at the present point in time. On the other hand,if it is determined that at a specific point in time, the real driver nolonger wants to change lanes or has just changed lanes, an imaginarydriver is not allowed to retain a lane-change intention to the right(LCR) at the next point in time . This is equally applicable to alane-change intention to the left (LCL). Accordingly, an imaginarydriver having a lane-change intention to the left (LCL) at a specificpoint in time is allowed to retain a lane-change intention to the left(LCL) at the next point in time upon determination that a lane changecontinues, but the imaginary driver is not allowed to continue to retaina lane-change intention to the left (LCL) at a specific point in timeupon failure to determine that the lane change continues. Therefore, animaginary driver that has one of the derivative lane-change intentions(LCR) and (LCL), is allowed to retain the derivative lane-changeintention at the next point in time upon determination that a lanechange continues.

As described above, a special memory portion is provided for storingintentions of the parent imaginary driver. The intentions include m, innumber, lane-keeping intentions (LK), over a period of time ranging fromtime (t) back to time (t−m+1). Each of the remaining memory portions isprovided for storing intentions for one of the additional imaginarydrivers. The intentions include lane-change intention (LCR) or (LCL)over a period of time ranging from time (t) back to time (t−m+1). It isnow apparent that, except for the special memory portion provided to theparent imaginary driver, the microcomputer resets memory portions forthe additional imaginary drivers upon determination that the memoryportion has contained m, in number, lane-change intentions.

Referring to FIG. 4, “SERIES L1” corresponds to a series of intentionsof an additional imaginary driver that is generated at time t, andincludes a lane-change intention to the right (LCR) at time (t). “SERIESL2” includes two lane-change intentions to the right (LCR) andrepresents intentions of another additional imaginary driver generatedearlier. “SERIES L3” includes (m−3), in number, lane-change intentionsto the right (LCR) and represents intentions of still another imaginarydriver that is generated earlier than “SERIES L1” and “SERIES L2.”

The imaginary driver corresponding to “SERIES L1” retains a lane-keepingintention (LK) at ever point in time from time (t−m+1) to time (t−1),and has a lane-change intention to the right (LCR) at the time (t). Theimaginary driver corresponding to “SERIES L2” retains a lane-keepingintention (LK) at every point in time from time (t−m+1) to time (t−2),and shifts to a lane-change intention to the right (LCR) at time (t−1).The imaginary driver corresponding to “SERIES L3” retains a lane-keepingintention (LK) at every point in time from time (t−m+1) to time (t−m+2),and shifts to a lane-change intention to the right (LCR) at time(t−m+3).

FIGS. 5( a) and 5(b) show rules that the imaginary driver's intentiongenerating section 40 (see FIG. 1) applies in determining an intentionfor each existing imaginary driver at each point in time. As mentionedbefore, the microcomputer allows a parent imaginary driver having alane-keeping intention (LK) at every point in time. As shown in FIG. 5(a), the microcomputer generates data related to two additional imaginarydrivers, each has one of two derivative lane-change intentions (LCR) and(LCL) based on a lane-keeping intention (LK) of the parent imaginarydriver at an immediately preceding point in time.

At each point in time, the microcomputer determines whether or not thevehicle's environment allows lane-change intentions are allowed tocontinue to exist at the next point in time by applying certain rules.FIG. 5( b) shows an exemplary rule used by the microcomputer. As shownin FIG. 5( b), an imaginary driver is allowed to retain a lane-changeintention to the right (LCR) at the next point in time, if it isdetermined that the real driver continues to retain a lane-changingintention at the present point in time. On the other hand, if it isdetermined that at a specific point in time, the real driver no longerwants to change lanes or has just changed lanes, an imaginary driver isnot allowed to retain a lane-change intention to the right (LCR) at thenext point in time. This is equally applicable to a lane-changeintention to the left (LCL). Accordingly, an imaginary driver having alane-change intention to the left (LCL) at a specific point in time isallowed to retain a lane-change intention to the left (LCL) at the nextpoint in time upon determination that a lane change continues, but theimaginary driver is not allowed to continue to retain a lane-changeintention to the left (LCL) at a specific point in time upon failure todetermine that the lane change continues. Therefore, an imaginary driverthat has one of the derivative lane-change intentions (LCR) and (LCL),is allowed to retain the derivative lane-change intention at the nextpoint in time upon determination that a lane change continues.

In the exemplary implementation, on one hand, the microcomputerdetermines that the lane-change intention may continue to exist if thevehicle continues to stay in the same lane. On the other hand, themicrocomputer determines that the lane-change intention has beenrealized if the vehicle has changed to a different lane. In other words,the microcomputer fails to determine that the lane-change intentioncontinues. Thus, lane-change intentions (LCR) and (LCL) at a specificpoint in time are allowed to continue to exist at the next point in timeupon determination that the vehicle continues to stay in the same lane.In contrast, lane-change intentions (LCR) and (LCL) are not allowed tocontinue to exist at the next point in time upon determination that thevehicle has changed to a different lane. As will be understood from thedescriptions below, all imaginary drivers (except for the parentimaginary driver) that have at least one derivative lane-changeintention (LCR) or (LCL)are terminated and reset upon determination thatthe vehicle has changed to a different lane.

At step S105, using the calculated operation amount Oid of eachimaginary driver (calculated at step S102) and the detected operationamount Ord of the real driver (detected at step S103), the microcomputercalculates a likelihood value Pid indicating how the calculatedoperation amount Oid of each imaginary driver approximates the detectedoperation amount Ord of the real driver. For illustration purpose, thelikelihood value Pid is used to represent a likelihood value Pid.lk ofan imaginary driver having a lane-keeping intention (LK), a likelihoodvalue Pid.lcr of an imaginary driver having a lane-change to the right(LCR), or a likelihood value Pid.lcl of an imaginary driver having alane-change intention to the left (LCL). In the exemplaryimplementation, the calculated operation amount Oid of each imaginarydriver is expressed by any one of the calculated steering angles θid.lk,θid.lcr, and θid.lcl. For illustration purpose, an imaginary driver'ssteering angle θid is used to represent any one of these calculatedsteering angles θid.lk, θid.lcr, and θid.lcl. In the exemplaryimplementation, the detected operation amount Ord of the real driver isexpressed by the detected steering angle θrd performed by the realdriver.

Many mathematical calculations can be used to compute the likelihoodvalue Pid. For example, the likelihood value Pid of each imaginarydriver is a logarithmic probability of a normalized value of theimaginary driver's steering angle θid relative to a normal distribution,where the mean (e) is the real driver's steering angle θrd and thevariance (σ) is a predetermined value ρrd such as a standard deviationof steering angles. Generally, the value of ρrd depends oncharacteristics of the vehicle, such as the steering gear ratio, and/orthe speed of the vehicle. ρrd may range from −15 degrees to +15 degrees,such as between 3 to 5 degrees. Of course, other values of ρrd may beused depending on the type and/or characteristics of vehicles. Thelikelihood value Pid is expressed as:Pid=log {Probn[(θid−θrd)/ρrd]}  (Eq. 7)where Probn is a probability density function that is used to calculatea probability with which a given sample is observed from a populationexpressed by the normal distribution.

At step S105, using before-mentioned equation Eq. 7, the microcomputercalculates a likelihood value Pid(t) for each of the imaginary driversof a dynamic family illustrated in FIG. 4. The calculated likelihoodvalues are stored in the memory portions corresponding to each imaginarydriver j, and are expressed as Pid(j)(t), where j corresponds to one ofthe imaginary drivers. Thus, Pid(j)(t) means a calculated likelihoodvalue for an imaginary driver j having an intention at time (t).

At step S106, using the stored likelihood valuesPid(j)(t)˜Pid(j)(t−m+1), the microcomputer calculates a collectivelikelihood value P(j)ids for each imaginary driver j that is designed tobehave as directed by intentions associated with each imaginary driverj. The collective likelihood value P(j)ids may be expressed as:

$\begin{matrix}{{{P(j)}{ids}} = {\prod\limits_{i = 1}^{m}{{{Pid}(j)}\left( {t - i + 1} \right)}}} & \left( {{Eq}.\mspace{14mu} 8} \right)\end{matrix}$Equation Eq. 8 states that the collective likelihood value P(j)ids isthe product of all of the calculated likelihood valuesPid(j)(t)˜Pid(j)(t−m+1).

At step S107, the microcomputer estimates a real driver's intention λrd.In this exemplary implementation, the microcomputer chooses one of theimaginary drivers that has the maximum calculated collective likelihoodvalues P(j)ids among all imaginary drivers. The series of intentionscorresponding to the chosen imaginary driver is now labeled Lmax. Then,the microcomputer chooses the latest intention of the series Lmax toapproximate a real driver's intention λrd. The real driver's intentionλrd may be expressed as:λrd=max[Pid(Lmax).lk(t), Pid(Lmax).lcr(t), Pid(Lmax).lcl(t)]  (Eq. 9)

At step S108, the microcomputer calculates a confidence index Sc of theintention λrd estimated at step S107. Referring to FIG. 4, that theparent imaginary driver has a lane-keeping intention (LK) at time (t).There is a plurality of additional imaginary drivers having derivativeintentions. The additional imaginary drivers may be divided into a LCRgroup and a LCL group. The LCR group consists of additional imaginarydrivers that has a lane-change intention to the right (LCR) at time (t).The LCL group consists additional imaginary drivers that has alane-change intention to the left (LCL) at time (t).

If the estimated real driver's intention λrd determined at step S107 isindicative of a lane-change intention to the right (LCR), the chosenseries Lmax belongs to the LCR group. The collective likelihood valueP(Lmax)ids is known and the maximum among the additional imaginarydrivers in the LCR group is selected. This known collective likelihoodvalue P(Lmax) corresponds to an imaginary driver having a lane-changeintention with the highest probability Pr(LC). In addition to theselection of the series Lmax, the parent imaginary driver is selectedand its collective likelihood value P(j=parent)ids is stored as animaginary driver having a lane-keeping intention with the highestprobability Pr(LK).

If the estimated real driver's intention λrd determined at step S107 isindicative of a lane-change intention to the left (LCL), the chosenseries Lmax belongs to the LCL group. The collective likelihood valueP(Lmax)ids is known and the maximum among to the additional imaginarydrivers in the LCL group is selected. This known collective likelihoodvalue P(Lmax)ids is stored as the imaginary driver having a lane-changeintention with the highest probability Pr(LC). In addition to theselection of the series Lmax, the parent imaginary driver is selectedand its collective likelihood value P(j=parent)ids is stored as theimaginary driver having a lane-keeping intention with the highestprobability Pr(LK).

If the estimated real driver's intention λrd determined at step S107 isindicative of a lane-keeping intention to the left (LK), the chosenseries Lmax correspond to the parent imaginary driver. The collectivelikelihood value P(Lmax)ids for the parent imaginary driver is known andstored as the imaginary driver having a lane-keeping intention with thehighest probability Pr(LK). In addition to the selection of the seriesLmax, among the additional imaginary drivers in the LCR group or LCLgroup, an additional imaginary driver that has the maximum collectivelikelihood values P(j)ids among all the additional imaginary drivers isselected, and its collective likelihood value valve P(j)ids is stored asthe imaginary driver having the lane-change intention with the highestprobability Pr(LC).

Referring to FIG. 6, if the estimated real driver's intention λrd isindicative of a lane change to the right (LCR), and an additionalimaginary driver corresponding to “SERIES L3” has the maximum collectivelikelihood values, “SERIES L3” is labeled Lmax and the collectivelikelihood value P(3)ids is stored as Pr(LC). The collective likelihoodvalue for the parent imaginary driver is stored as Pr(LK).

For illustration purpose only, the lane-change intention to the right(LCR) and the lane-change intention to the left (LCL) are collectivelyrepresented as lane-change (LC). Using the values stored as Pr(LC) andPr(LK), the confidence index Sc may be expressed as:Sc=1/{1+exp(−2×k×Pr(LC)/Pr(LK))}  (Eq. 10)

where: k is an appropriate coefficient, which is usually setapproximately to 1 (such as 0.8, 0.9 or 1.0).

The confidence index Sc expressed by the equation Eq. 10 has a rangebetween 0 to 1. The confidence index Sc increases as the probabilitystored as Pr(LC) increases relative to the probability stored as Pr(LK).When the probability for LC and that for LK are 50:50, the confidenceindex Sc is 0.5 (Sc=0.5). When the probability for LC is 1, theconfidence index Sc is 1 (Sc=1).

At step S109, the microcomputer generates an output including theestimated real driver's intention λrd and confidence index Sc.

The flow chart in FIG. 7 illustrates a control routine of a driverassisting control program stored in the controller 150. The execution ofthe control routine is repeated at regular interval of, for example, 50msec.

In FIG. 7, at step S201, the controller 150 recognize environment in afield around the host vehicle. In particular, the controller 150receives, as inputs, signals of the laser radar 110, front camera 120and vehicle speed sensor 140 by reading operations to acquire dataregarding the vehicle's status and the vehicle's environment. Forexample, imaging a traffic scene where the host vehicle is following thepreceding vehicle, the acquired data include a vehicle speed V1 of thehost vehicle, a vehicle speed V2 of the preceding vehicle, and arelative speed to the preceding vehicle Vr. The relative speed Vr may beexpressed as Vr=V2−V1. The acquired data may include a coordinate X1 ofthe host vehicle and a coordinate X2 of the preceding vehicle, and adistance D to the preceding vehicle. The distance D may be expressed asD=X2−X1.

At step S202, the controller 150 calculates a risk potential RPassociated with the vehicle based on time to collision TTC and timeheadway THW, which are used as two exemplary notions to calculate therisk potential RP.

The TTC is an estimated period of time before the distance D becomeszero if the relative speed Vr to the preceding vehicle remainsunchanged. The TTC may be expressed as:TTC=−D/Vr  (Eq. 11)

The smaller the value of TTC, the more imminent is a collision is likelyto occur. In the traffic scene where the host vehicle is following thepreceding vehicle, most vehicle drivers perceived a high degree of riskand initiated deceleration to avoid collision well before the TTCbecomes less than 4 seconds. To some extent, the TTC is a goodindication for predicting future behaviors of the vehicle driver.However, when it comes to quantifying the degree of risk, which thevehicle driver actually perceives, TTC alone is insufficient to quantifythe degree of risk.

For instance, in a scenario in which the relative speed Vr is zero. Inthis case, the TTC is infinite irrespective of how narrow the distance Dis. However, in reality, the driver perceives an increase in the degreeof risk in response to a reduction in the distance D, accounting for anincrease in influence on the TTC by an unpredictable drop in a vehiclespeed of the preceding vehicle.

To address the above-mentioned discrepancy, the notion of time headwayTHW has been introduced to quantify an increase of an influence on TTCby an unpredictable drop in the vehicle speed of the preceding vehicle.THW is a period of time between the preceding vehicle reaching aspecific location and when the following vehicle reaching the samelocation, at which point in time, THW is reset. The THW is expressed as,THW=D/V1  (Eq. 12)

In the case where the host vehicle is following the preceding vehicle,the vehicle speed V2 of the preceding vehicle may be used instead of thevehicle speed V1 in Eq. 12.

The relationship between the two notions TTC and THW is such that achange in vehicle speed V2, if any, of the preceding vehicle will resultin a small change in the value of TTC when the THW is large, but thesame change in vehicle speed V2 of the preceding vehicle will result ina large change in the value of TTC when the THW is small.

In this exemplary implementation, the risk potential RP calculated atstep S202 is expressed as a sum of a first index and a second index. Thefirst index represents a degree that the vehicle has approached thepreceding vehicle. The second index represents a degree that anunpredictable change in vehicle speed V2 of the preceding vehicle mighthave influence upon the vehicle. The first index may be expressed as afunction of the reciprocal of time to collision TTC, and the secondindex may be expressed as a function of the reciprocal of time headwayTHW. The risk potential RP may be expressed as:RP=a/THW+b/TTC  (Eq. 13)

where: b and a (b>a) are parameters weighting 1/TTC and 1/THW,respectively, such that 1/THW is less weighted than 1/TTC. The values ofa and b are optimized after accounting for a statistics of values of THWand TTC collected in a traffic scene including the host vehicle isfollowing the preceding vehicle. In this exemplary implementation, b=8and a=1.

At step S211, the controller 150 receives, as an input, an acceleratorpedal stroke S by reading operation of the output of the acceleratorpedal stroke sensor 181.

At step S212, the controller 150 calculates an accelerator pedalreaction force instruction value FA. First, the controller 150calculates a reaction force increment ΔF in response to the riskpotential RP by, for example, referring to the characteristic curveshown in FIG. 8.

The curve in FIG. 8 shows characteristics of reaction force increment ΔFrelative to different values of risk potential RP by the driver from thepreceding vehicle. When the risk potential RP is smaller than a minimumvalue RPmin, the reaction force increment ΔF is always zero to preventforwarding unnecessary information to the driver. An appropriate valueof RPmin can be determined and set empirically.

When the risk potential RP exceeds the minimum value RPmin, the reactionforce increment ΔF increases exponentially as the risk potential RPincreases. The reaction force increment ΔF within this region may beexpressed as:ΔF=k·RP ^(n)  (Eq. 14)where: k and n are constants that are appropriately determined based onresults obtained by drive simulator and field drive to provide smoothconversion of the risk potential RP to the reaction force increment ΔF.

The controller 150 calculates the sum of the reaction force increment ΔFand the ordinary reaction force characteristic to give the acceleratorpedal reaction force instruction value FA.

At step S213, the controller 150 reads, as an input, the estimated realdriver's intention λrd determined by the real driver's intentionestimating system 1.

At step S214, the controller 150 determines whether or not the estimateddriver's intention λrd is indicative of a lane-change intention. If thisis the case, the logic goes to step S215.

At step S215, the controller 150 reads the confidence index Sc for alane-change intention, which is determined as the real driver'sintention estimating system 1.

At step S216, the controller 150 corrects the accelerator pedal reactionforce instruction value FA to provide a corrected accelerator pedalreaction force instruction value FAc. In this exemplary implementation,the accelerator pedal reaction force instruction value FA is processedby a low-pass filter and decreased. In this case, the correctedaccelerator pedal reaction force instruction value FAc may be expressedas:FAc=gf _(F)(FA)=kf·{1/(1+Tsf)}·FA  (Eq. 15)

where: kf is an appropriately determined constant, and Tsf is a timeconstant of the low-pass filter, which is determined as a function ofthe confidence index Sc and may be expressed as:Tsf=f _(f)(Sc)  (Eq. 16)

FIG. 9 illustrates characteristics of function f_(f)(Sc). As illustratedin FIG. 9, the time constant Tsf decreases as the confidence index Scincreases, which allows a faster reduction in the accelerator pedalreaction force.

If, at step S214, the controller 150 determines that the estimateddriver's intention λrd is indicative of a lane-keeping intention (LK),the logic goes to S209.

At step S217, the controller 150 sets the accelerator pedal reactionforce instruction value FA as the corrected accelerator pedal reactionforce instruction value FAc.

At step S218, the controller 150 provides, as an output, the correctedaccelerator pedal reaction force instruction value FAc that has beendetermined at step S216 or S217 to the accelerator pedal reaction forcecontrol unit 170.

The accelerator pedal reaction force control unit 170 controls the servomotor 180 in response to the corrected accelerator pedal reaction forceinstruction value FAc.

The exemplary implementation provides effects as follows:

(1) The driver's intention estimating system 1 estimates a real driver'sintention λrd based on a status of a host vehicle operated by the realdriver and a state of environment around the vehicle, and calculates aconfidence index Sc of an estimated intention λrd of the real driver.With confidence index Sc, it is possible to evaluate how determined thereal driver is in performing an operation based on the estimatedintention λrd. The driver assisting system 100 modifies an acceleratorpedal reaction force based on the estimated results at the driver'sintention estimating system 1 during regulation of the accelerator pedalreaction force based on a risk potential RP associated with the vehicle.This approach allows regulation of the accelerator pedal reaction forceto meet the driver's intention and to provide a feedback to the driverindicating a risk potential RP by applying a reaction force to thedriver.

(2) The driver's intention estimating system 1 is configured todynamically create data related to a plurality of imaginary drivers. Foreach of imaginary driver j, a collective likelihood value P(j)ids iscalculated. The driver's intention estimating system 1 estimates a realdriver's intention λrd by comparing the collective likelihood valuesP(i)ids among all the imaginary drivers. Therefore, an estimate of thereal driver's intention can be obtained with better accuracy.

In the exemplary implementation, the confidence index Sc is calculatedfor every estimated real driver's intention λrd. One may calculate theconfidence index only when the estimated real driver's intentionindicates a lane-change intention.

In the descriptions above, steering angles θrd and θid are used asoperations Ord and Oid of the real and imaginary drivers. The presentdisclosure is not limited to this specific embodiment, as many otheroperations can also be applied. For example, an accelerator pedal strokeinstead of a steering angle can be used. In this case, an acceleratorpedal stroke Sid of an imaginary driver may be calculated based on adegree to which the vehicle has approached the preceding vehicle. Thisdegree may be expressed by distance to the preceding vehicle and timeheadway THW. A likelihood value of the accelerator pedal stroke Sid withrespect to an accelerator pedal stroke Srd of a real driver iscalculated for use in estimating a real driver's intention.

In the preceding description, two reference points are provided for oneof the intentions as shown in FIG. 3. In application, any desired numberof reference points may be provided.

Furthermore, in the previous descriptions, series of intentionscorresponding to imaginary drivers are dynamically created forcalculation of operation amounts Oid. An estimated intention λrd is thendetermined by comparing the calculated operation amounts of theimaginary drivers relative to the operation amount of a real driver.However, the estimated intention of the real driver can be obtainedusing many different approaches. For instance, it is possible toestimate a real driver's intention by comparing predetermined referenceoperation patterns wit the actual operation pattern of a real driver.

In order to determine reference operation patterns, certain mathematicalor statistical tools may be used. For example, the support vectormachine (SVM) and relevance vector machine (RVM) are techniques forpattern recognition for detecting a real driver's intention. The SVM isa good technique for classification of non-parametric patterns. The SVMis a technique to find a separating plane by maximizing the marginbetween the separating plane and input patterns to be separated, and isexpressed by an equation as a linear separator according to kernelcharacteristics. Using this equation, the separating plane by the SVMcan be obtained as a solution to the second order optimization problem.The RVM proposed by Tipping is a kernel learning system. According tothe RVM technique, in estimating a function out of data obtained bylearning, a preliminary distribution with its mean being zero is given,and the EM algorithm is used to optimize its variance with respect tolikelihood of weighting parameters.

Using the SVM or RVM technique, it is necessary to learn beforehand acorrect pattern for operating a vehicle corresponding to the subjects (alane-keeping intention and a lane-change intention) to be recognized.Upon actual recognition, it is needed to provide on a real time basis anactual pattern for operating a vehicle by a real driver. It isdetermined whether the real driver's intention is a lane-keepingintention or a lane-change intention based on the coincidence betweenthe learned pattern and the actual pattern.

A discriminant, which may be used in SVM and RVM, may be expressed as:

$\begin{matrix}{{y(x)} = {{\sum\limits_{n = 1.}^{N}{w_{n}{K\left( {x,x_{n}} \right)}}} + w_{0}}} & \left( {{Eq}.\mspace{14mu} 17} \right)\end{matrix}$

In the equation Eq. 17, w_(n) is a predetermined, by learning,recognition parameter for input of a plurality of operation amounts (forexample, a steering angle, an accelerator pedal stroke) and a pluralityof vehicle status amounts (for example, a vehicle speed, a lateraldistance to a centerline of a lane) to K. The result y(x) of theequation Eq. 17 provides a discrimination whether the real driver'sintention is a lane-keeping intention or a lane-change intention. The xand xn are unknown values representing actual operations, such assteering operation angles, gas pedal depression angles or vehicle speed.Wm is a predetermined parameter that represents a learned condition.

In using SVM or RVM, the real driver's intention λrd may be estimated asa result of pattern discrimination. Using the degree of coincidencebetween the patterns makes it possible to calculate a confidence indexSc with regard the estimated real driver's intention λrd. If patterndiscrimination technique is used, the confidence index Sc may be used toquantity how determined the real driver is to perform an operationaccording to the intention λrd.

Second Exemplary Implementation of the Disclosure

Referring to FIGS. 10 to 12, another exemplary implementation of adriver assisting system 200 is described. The driver assisting system200 is substantially the same as the driver assisting system 100illustrated in FIG. 1( b). Thus, like reference numerals are used todesignate like parts or portions throughout FIGS. 1( b) and 10. However,the driver assisting system 200 is different from the driver assistingsystem 100 in that a controller 150A includes a deceleration controlinstruction value Xg calculator 154 and an instruction value Xgcorrecting section 155. In addition, an automatic brake control unit 190is provided.

A RP calculator 151 provides a risk potential RP to both an acceleratorpedal reaction force instruction value FA calculator and decelerationcontrol instruction value Xg calculator 154. The deceleration controlinstruction value Xg calculator 154 calculates a deceleration controlinstruction value Xg based on the risk potential RP and provides, as anoutput, a deceleration control instruction value Xg to the instructionvalue Xg correcting section 155. The instruction value Xg correctingsection 155 corrects the instruction value Xg based on the estimateddriver's intention λrd and the confidence index Sc, and provides, as anoutput, a corrected instruction value Xgc to the automatic brake controlunit 190.

In response to the corrected instruction value Xgc from the controller150A, the automatic brake control unit 190 provides, as an output, abrake pressure instruction value to wheel brake actuators fordecelerating the vehicle.

The flow chart in FIG. 13 illustrates a control routine of a driverassisting control program stored in the controller 150A. The executionof the control routine is repeated at regular interval of, for example,50 msec.

The flow chart in FIG. 13 is substantially the same as the flow chartshown in FIG. 7 except for new steps S221 to S277 performing steps ofcalculating the deceleration control instruction value Xg, correctingthe instruction value Xg, and generating a corrected instruction valueXgc. Like reference numerals are used to designate like steps throughoutFIGS. 7 and 13.

In FIG. 13, at step S221, the controller 150A calculates thedeceleration control instruction value Xg based on the risk potential RPas shown in FIG. 16. FIG. 16 illustrates the relationship between theinstruction value Xg and the risk potential RP.

The curve in FIG. 16 shows characteristics of the decelerationinstruction value Xg relative to different values of risk potential RP.When the risk potential RP decreases below a minimum value RPmin, thedeceleration instruction value Xg is always zero in order to preventforwarding deceleration shocks to the driver. An appropriate value forRPmin is empirically determined and set.

If risk potential RP exceeds the minimum value RPmin, the decelerationinstruction value Xg increases exponentially as the risk potential RPincreases.

At step S222, the controller 150A reads, as an input, the estimated realdriver's intention λrd determined by the real driver's intentionestimating system 1.

At step S223, the controller 150A determines whether or not theestimated real driver's intention λrd is indicative of a lane-changeintention. If this is the case, the logic goes to step S224.

At step S224, the controller 150A reads, as an input, the confidenceindex Sc, for the a lane-change intention, which is determined as theestimated intention of the real driver by the driver's intentionestimating system 1.

At step S225, the controller 150A corrects the deceleration instructionvalue Xg to provide a corrected deceleration instruction value Xgc. Inthis exemplary implementation, the deceleration instruction value Xg isprocessed by a low-pass filter and decreased. In this case, thecorrected deceleration instruction value Xgc may be expressed as:Xgc=gf _(G)(Xg)=kg·{1/(1+Tsg)}·Xg  (Eq. 18)

where: kg is an appropriately determined constant and usually setapproximately to 1 (such as 0.8, 0.9 or 1.0), and Tsg is a time constantof the low-pass filter, which is determined as a function of theconfidence index Sc and may be expressed as:Tsg=f _(g)(Sc)  (Eq. 19)

The curve shown in dotted line in FIG. 15 illustrates the functionf_(g)(Sc). As shown in FIG. 15, the time constant Tsg decrease as theconfidence index Sc increases over 0.5, which allows a fast reduction inthe accelerator pedal reaction force.

If, at step S223, the controller 150A determines that the estimateddriver's intention λrd is indicative of a lane-keeping intention (LK),the logic goes to S226.

At step S226, the controller 150A sets the deceleration instructionvalue Xg as the corrected deceleration instruction value Xgc.

At the next step S227, the controller 150A provides, as outputs, thecorrected accelerator pedal reaction force instruction value Fac and thecorrected deceleration instruction value Xgc to the accelerator pedalreaction force control unit 170 and to the automatic brake control unit190.

Referring to FIGS. 17( a) to 17(b), the driver assisting system 200 isfurther described. FIG. 17( a) illustrates a traffic scene in which thehost vehicle changes lanes to pass the preceding vehicle. FIGS. 17( b)and 17(b) illustrate changes of the corrected accelerator pedal reactionforce instruction value Fac relative to time, and changes of thecorrected deceleration instruction value Xgc relative to time during alane-change operation. The moment at which the estimated real driver'sintention λrd indicating a lane-change intention is time ta. In FIGS.17( b) and 17(c), the curves in solid lines show changes of theinstructions values FAc and Xgc when the confidence index Sc is 0.6(Sc=0.6). The curves in dotted lines show changes of the instructionvalues FAc and Xgc when the confidence index Sc is 0.8 (Sc=0.8).

As readily seen from FIGS. 17( b) and 17(c), the risk potential RPincreases as the host vehicle approaches the preceding vehicle. Inresponse to the increase of the risk potential RP, the correctedaccelerator pedal reaction force instruction value FAc and the correcteddeceleration instruction value Xgc increase. At time ta, the estimatedreal driver's intention λrd indicates a lane-change intention.Immediately after the moment ta, the instruction values FAc and Xgc dropgradually. The instruction values FAc and Xgc drop faster as theconfidence index Sc becomes larger.

Immediately after the driver has made up his/her mind to change lanes,the accelerator pedal reaction force and the deceleration are regulatedto meet expectations of the driver without hampering the driver's actionto change lanes. With the same confidence index Sc, the correctedaccelerator pedal reaction force instruction value FAc drops faster thanthe corrected deceleration instruction value Xgc. Thus, a drop inaccelerator pedal reaction force notifies the driver that the driverassisting system is ready for his/her lane-change intention.Subsequently, the deceleration control is gradually terminated, toalleviate shocks applied to the driver.

This exemplary implementation provides the following effects:

(1) The controller 150A regulates an accelerator pedal reaction forcebased on a risk potential RP determined according to environmentsurrounding a vehicle. The controller 150A modifies the acceleratorpedal reaction force based on an estimated driver's intention λrd and aconfidence index Sc. Modifying the accelerator pedal reaction forcebased on estimated driver's intention λrd and confidence index Sc makesit possible to reflect the driver's intention in regulation ofaccelerator pedal reaction force and continue to provide feedbacksrelated to the state of environment surrounding the vehicle by applyingthe reaction force.

(2) The controller 150A allows the accelerator pedal reaction force todecrease as the confidence index Sc becomes higher upon determinationthat the estimated driver's intention λrd indicates a lane-changeintention. Accordingly, the accelerator pedal reaction force dropsfaster when the confidence index Sc is higher, to meet the driver'sdemand related to the lane-change intention.

(3) The controller 150A is provided with an accelerator pedal reactionforce instruction value FA correcting section 153 that corrects therelationship between the risk potential RP and the reaction force inresponse to the confidence index upon detection of a lane-changeintention of the real driver. Thus, when the confidence index Sc becomeshigher, the accelerator pedal reaction force quickly drops, to meet thedriver's demand with respect to the lane-change intention.

(4) The controller 150A regulates a vehicle deceleration based on a riskpotential RP determined according to environment surrounding a vehicle.The controller 150A modifies the vehicle deceleration based on anestimated driver's intention λrd and a confidence index Sc. Modifyingthe vehicle deceleration based on estimated driver's intention λrd andconfidence index Sc makes it possible to reflect the driver's intentionin regulation of vehicle deceleration and continue to provide feedbacksrelated to the risk from the preceding vehicle.

(5) The controller 150A allows the vehicle deceleration to decrease asthe confidence index Sc increases upon determination that the estimateddriver's intention λrd is a lane-change intention. Accordingly, thevehicle deceleration drops quickly when the confidence index Sc is high,thus meeting driver's demand related to the lane-change intention.

(6) The controller 150A is provided with a vehicle deceleration controlinstruction value Xg correcting section that corrects the relationshipbetween the risk potential RP and the vehicle deceleration in responseto the confidence index upon determination of a lane-change intentionretained by the real driver. Thus, when the confidence index Sc becomeshigher, the accelerator pedal reaction force quickly drops, thus meetingthe driver's demand in connection with the lane-change intention.

(7) The controller 150A allows the reaction force to drop at a rategreater than that of the vehicle deceleration, relative to the sameconfidence index Sc upon determination that the estimated driver'sintention is a lane-change intention. As shown in FIG. 15, this has beenaccomplished by setting the time constant Tsf being shorter than thetime constant Tsg relative to the same confidence index Sc. Thus, upondetermination that the estimated driver's intention λrd is a lane-changeintention, the accelerator pedal reaction force drops to notify thedriver that the driver assisting system is ready for the driver'sintention before the vehicle deceleration drops gradually, thuseffectively alleviating unpleasant feeling being felt by the driver.

In the exemplary implementation illustrated in FIG. 10, the acceleratorpedal reaction force instruction value FA and the vehicle decelerationinstruction value Xg are corrected based on the confidence index Sc,This is only one of many examples that the concepts of this disclosuremay be implemented. Another example is to correct a risk potential RPbased on the confidence index Sc, and use the corrected risk potentialRP to calculate the accelerator pedal reaction force instruction valueFA and the vehicle deceleration instruction valve Xg. Instead ofmodifying the time constants Tsf and Tsg, it is possible to provide timeconstant terms (k1×Tsf) and (k2×Tsg) by setting predetermined values forTsf and Tsg, and allowing coefficients k1 and k2 to change withdifferent values of confidence index Sc.

In the exemplary implementation illustrated in FIG. 10, a risk potentialRP is applied to calculations of both accelerator pedal reaction forceregulation and vehicle deceleration regulation. This is just one of manyexamples that the concepts of this disclosure may be implemented.Another example is using the risk potential RP for only one of theaccelerator pedal reaction force control and vehicle decelerationcontrol.

In the exemplary implementation illustrated in FIG. 10, the riskpotential RP is determined by the time to collision TTC and time headwayTHW. The risk potential RP may be determined suing other approaches. Forinstance, the reciprocal of TTC may be utilized to generate a riskpotential RP. In the exemplary implementation illustrated in FIG. 10,the same risk potential RP is used for determining the accelerator pedalreaction force instruction value FA and the vehicle deceleration controlinstruction value Xg. However, more than one risk potentials RP may beused to implement the system of this disclosure. For instance, twodifferent risk potential RP may be used for determining the acceleratorpedal reaction force instruction value FA and the vehicle decelerationcontrol instruction value Xg, respectively. In this case, the riskpotential RP for the vehicle deceleration control instruction value Xgmust be set accounting for the relationship between the host and thepreceding vehicles.

Although the disclosure has been shown and described with respect to theexemplary implementations, it is obvious that equivalent alterations andmodifications will occur to those skilled in the art upon reading andunderstanding of the specification. The present disclosure includes allsuch equivalent alterations and modifications, and is limited only bythe scope of the claims.

1. A system for estimating an intention of an operator of a machinecomprising: a detector configured to detect an operation performed bythe operator, wherein the operation may correspond to multiple possibleintentions retained by the operator; an intention estimation deviceconfigured to generate an estimated intention of the operator based onthe detected operation; and a confidence calculator configured tocalculate a confidence index of the estimated intention.
 2. The systemof claim 1, wherein: the intention estimation device includes: a firstdevice configured to provide data related to a plurality of imaginaryoperators, each of the plurality of imaginary operators associated withat least one intention, wherein each of the at least one intention isassociated with an operation of the respective imaginary operator; asecond device configured to calculate a likelihood value for each of theplurality of imaginary operators based on the detected operation of theoperator and the respective associated operation of each of theplurality of imaginary operators; and a third device configured togenerate the estimated intention of the operator based on the respectivelikelihood value of each of the plurality of imaginary operators; andthe confidence calculator is configured to calculate the confidenceindex of the estimated intention based on the respective likelihoodvalue of each of the plurality of imaginary operators.
 3. The system ofclaim 1, wherein the intention estimation device generates the estimatedintention of the operator based on the detected operation of the driverand reference data related to predetermined operation patterns.
 4. Thesystem of claim 3, wherein the intention estimation device generates theestimated intention by applying one of a support vector machine and arelevance vector machine to data related to the detected operation andthe reference data related to the predetermined operation patterns. 5.The system of claim 1 further comprising a control device configured toregulate the operation of an operation device of a the machine based onthe confidence index.
 6. The system of claim 5, wherein: the machine isa vehicle; the operation device is an accelerator pedal of the vehicleor a braking system of the vehicle; and the control device modifies areaction force of the accelerator pedal of the vehicle or a decelerationforce of the braking system.
 7. The system of claim 6 further comprisinga risk calculation device configured to calculate a risk potentialassociated with the vehicle; wherein the control device regulates theoperation device of the vehicle based on the calculated risk potentialassociated with the vehicle and the confidence index.
 8. The system ofclaim 7, wherein the control device modifies the risk potential based onthe confidence index, and regulates the operation of the operationdevice based on the modified risk potential.
 9. The system of claim 7,wherein the control device calculates a regulation amount to regulatethe operation of the operation device based on the risk potential, andmodifies the calculated regulation amount based on the confidence index.10. A method for estimating an intention of an operator of a machinecomprising the steps of: detecting an operation performed by theoperator, wherein the operation may correspond to multiple possibleintentions retained by the operator; generating an estimated intentionof the operator based on the detected operation; and calculating aconfidence index of the estimated intention.
 11. The method of claim 10,wherein: the step of generating an estimated intention includes thesteps of: providing data related to a plurality of imaginary operators,each of the plurality of imaginary operators associated with at leastone intention, wherein each of the at least one intention is associatedwith an operation of the respective imaginary operator; calculating alikelihood value for each of the plurality of imaginary operators basedon the detected operation of the operator and the respective associatedoperation of each of the plurality of imaginary operators; andgenerating the estimated intention of the operator based on therespective likelihood value of each of the plurality of imaginaryoperators; and the step of calculating the confidence index calculatesthe confidence index of the estimated intention based on the respectivelikelihood value of each of the plurality of imaginary operators. 12.The method of claim 10, wherein the step of generating the estimatedintention generates the estimated intention of the operator based on thedetected operation of the driver and reference data related topredetermined operation patterns.
 13. The method of claim 12, whereinthe step of generating the estimated intention generates the estimatedintention by applying one of a support vector machine and a relevancevector machine to data related to the detected operation and thereference data related to the predetermined operation patterns.
 14. Themethod of claim 10 further comprising the step of regulating theoperation of an operation device of a the machine based on theconfidence index.
 15. The method of claim 14, wherein: the machine is avehicle; the operation device is an accelerator pedal of the vehicle ora braking system of the vehicle; and the regulating step modifies areaction force of the accelerator pedal of the vehicle or a decelerationforce of the braking system.
 16. The method of claim 15 furthercomprising the step of calculating a risk potential associated with thevehicle; wherein the regulating step regulates the operation device ofthe vehicle based on the calculated risk potential associated with thevehicle and the confidence index.
 17. The method of claim 16, whereinthe regulating step includes the steps of: modifying the risk potentialbased on the confidence index; and regulating the operation of theoperation device based on the modified risk potential.
 18. The method ofclaim 16, wherein the regulating step includes the steps of: calculatinga regulation amount to regulate the operation of the operation devicebased on the risk potential; and modifying the calculated regulationamount based on the confidence index.
 19. A system for estimating anintention of an operator of a machine comprising: means for detecting anoperation performed by the operator, wherein the operation maycorrespond to multiple possible intentions by the operator; means forgenerating an estimated intention of the operator based on the detectedoperation; and means for calculating a confidence index of the estimatedintention.
 20. A machine-readable medium bearing instructions forcalculating an estimated intention of an operator, the instructions,upon execution by a data processing system, causing the data processingsystem to perform the steps of: detecting an operation performed by theoperator, wherein the operation may correspond to multiple possibleintentions retained by the operator; generating an estimated intentionof the operator based on the detected operation; and calculating aconfidence index of the estimated intention.
 21. A vehicle comprising: adetector configured to detect an operation performed by a driver of thevehicle, wherein the operation may correspond to multiple possibleintentions retained by the driver; an intention estimation deviceconfigured to generate an estimated intention of the driver based on thedetected operation; and a confidence calculator configured to calculatea confidence index of the estimated intention.
 22. The vehicle of claim21 further including a control the operation of the vehicle based on theconfidence index.